Gender classification for real-time audience analysis system

The system allowing to extract all the possible information about depicted people from the input video stream is discussed. As reported previously, the proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. The crucial part of the system is gender classifier construction on the basis of machine learning methods. We propose a novel algorithm consisting of two stages: adaptive feature extraction and support vector machine classification. Both training technique of the proposed algorithm and experimental results acquired on a large image dataset are presented. More than 90% accuracy of viewer's gender recognition is achieved

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Gender in Engineering Departments: Are There Gender Differences in Interruptions of Academic Job Talks?

We use a case study of job talks in five engineering departments to analyze the under-studied area of gendered barriers to finalists for faculty positions. We focus on one segment of the interview day of short-listed candidates invited to campus: the “job talk”, when candidates present their original research to the academic department. We analyze video recordings of 119 job talks across five engineering departments at two Research 1 universities. Specifically, we analyze whether there are differences by gender or by years of post-Ph.D. experience in the number of interruptions, follow-up questions, and total questions that job candidates receive. We find that, compared to men, women receive more follow-up questions and more total questions. Moreover, a higher proportion of women’s talk time is taken up by the audience asking questions. Further, the number of questions is correlated with the job candidate’s statements and actions that reveal he or she is rushing to present their slides and complete the talk. We argue that women candidates face more interruptions and often have less time to bring their talk to a compelling conclusion, which is connected to the phenomenon of “stricter standards” of competence demanded by evaluators of short-listed women applying for a masculine-typed job. We conclude with policy recommendations

Reading porn: the paradigm shift in pornography research

This paper examines the paradigm shift in pornography theory and research from a focus on 'texts and effects' through to work emerging from the late 1980's onwards. The paper considers the reconceptualisation of pornography as a category, the location of pornography in relation to cultural hierarchy and form, the changing status of pornography in relation to mainstream representations, the significance of developing technologies and the movement towards more situated accounts of pornographic texts and their audiences as a series of attempts to contextualise the question 'what is pornography?'</p